Optimasi Fuzzy Time Series Dengan Algoritme Genetika Untuk Meramalkan Jumlah Pengangguran di Jawa Timur - Radifah Radifah; Budi Darma Setiawan; Rendi Cahya Wihandika (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2018)
Unemployment becomes one of the important points that are occurred in Indonesia. High unemployment rate has an impact on the economic and poverty levels of Indonesians especially in East Java. The increase number of unemployment can reduce the income and productivity of society. Several factors that are causing the increase of unemployment make the government difficult to overcome the numbers of unemployment annually that experience ups and downs. So, by predicting the number of unemployment in East Java, it can facilitate the government in overcoming the unemployment rate and expanding the workforce especially in East Java. The method that is used in this study is Fuzzy Time Series that use Genetic Algorithm. The best genetic algorithm parameter values are by testing to the genetic algorithm parameters and producing the best average fitness value. The result of genetic algorithm parameter test are with the population size of 525, the combination of crossover rate and mutation rate of 0,8 and 0,2 and at generation of 1200 which reaches the most optimal average fitness value is 13,840314614 with Root Mean Square Error(RMSE) value equal to 0,0722526928.